On many online portals or merchant websites, users generate and post reviews about products and services. For example, a user may write a review of a digital camera along with a rating such as four stars. While user generated reviews are helpful, the quality of the reviews may vary. For example, some reviews may have many misspellings, may consist of only a few words, may include profanity, or may be spam and have nothing to do with the associated product. To help users filter out the low quality reviews, many online portals may allow other users to rate or comment on the quality of reviews. For example, a user may give a review a “thumbs up” or “thumbs down”, or may give the review a numerical score based on the perceived quality of the review. Other users may then only be showed reviews having high quality ratings, may choose to only see reviews of high quality, or may be shown reviews in order of perceived quality.
However, the above described solutions have several problems. One such problem is with how to treat reviews that have no ratings. For example, a review may have just been posted and may not have been rated yet, or a review may be for a product that is new or not popular and may not have received any reviews. Another problem is that users may score reviews not based on the quality of the review, but based on their agreement with the opinions expressed by the review. For example, a user may give a “thumbs down” to all negative reviews for a particular product, regardless of the actual quality of the review. Further, many websites now aggregate reviews from a plurality of review websites, and it is difficult to determine scores for the aggregated reviews and to integrate the scores of reviews from different review websites.